Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes

Capacity management is critical for software organizations to allocate resources effectively and meet operational demands. An important step in capacity management is predicting future resource needs often relies on data-driven analytics and machine learning (ML) forecasting models, which require fr...

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Bibliographic Details
Published in:Proceedings - International Symposium on Software Reliability Engineering pp. 394 - 405
Main Authors: Poenaru-Olaru, Lorena, van 't Hof, Wouter, Stando, Adrian, Trawinski, Arkadiusz P., Kapel, Eileen, Rellermeyer, Jan S., Cruz, Luis, van Deursen, Arie
Format: Conference Proceeding
Language:English
Published: IEEE 21.10.2025
Subjects:
Accuracy
Adaptation models
Computational modeling
concept drift detection
Data models
Detectors
Forecasting
Predictive models
retraining based on drift detection
Software
Software reliability
Time series analysis
time series forecasting
ISSN:2332-6549
Online Access:Get full text
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Summary:Capacity management is critical for software organizations to allocate resources effectively and meet operational demands. An important step in capacity management is predicting future resource needs often relies on data-driven analytics and machine learning (ML) forecasting models, which require frequent retraining to stay relevant as data evolves. Continuously retraining the forecasting models can be expensive and difficult to scale, posing a challenge for engineering teams tasked with balancing accuracy and efficiency. Retraining only when the data changes appears to be a more computationally efficient alternative, but its impact on accuracy requires further investigation. In this work, we investigate the effects of retraining capacity forecasting models for time series based on detected changes in the data compared to periodic retraining. Our results show that drift-based retraining achieves comparable forecasting accuracy to periodic retraining in most cases, making it a costeffective strategy. However, in cases where data is changing rapidly, periodic retraining is still preferred to maximize the forecasting accuracy. These findings offer actionable insights for software teams to enhance forecasting systems, reducing retraining overhead while maintaining robust performance.
ISSN:2332-6549
DOI:10.1109/ISSRE66568.2025.00047